Competitive intelligence as it relates to pricing has been an important aspect of the retail business for decades. Today, via the internet consumers have tools that allow them to compare prices across thousands of retailers in seconds.
Many retailers carry a very large number of products on their catalog, often times in excess of 100,000 different stock keeping units (SKUs) associated with different products. Each SKU is often sold by many different competitors at different prices. However, competitors may change their prices for products at any time, which makes it more difficult to determine the pricing of the products at different retailers. Different retailers selling a plurality of products at different prices create a massive amount of information to be analyzed on a timely manner. Because of the massive amount of information associated with competitive intelligence, oftentimes retailers find themselves with product prices that are either too high or too low. A retailer having sub-optimal product pricing can results in either low sales or poor margins.
Current price optimization and sensitivity analysis techniques rely primarily on historical pricing data and consumer facing website click stream data. These solutions simply react to fluctuating sales volumes and do not take into account how competitors react and respond to price changes to a product on a channel, pricing and promotions and freight pricing strategies. To this end, there is a need for improved systems and methods for revenue management, price sensitivity analysis and price optimization utilizing competitive data and web analytics.